The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community

The EMBnet Conference 2008, focusing on 'Leading Applications and Technologies in Bioinformatics', was organized by the European Molecular Biology network (EMBnet) to celebrate its 20th anniversary. Since its foundation in 1988, EMBnet has been working to promote collaborative development of bioinformatics services and tools to serve the European community of molecular biology laboratories. This conference was the first meeting organized by the network that was open to the international scientific community outside EMBnet. The conference covered a broad range of research topics in bioinformatics with a main focus on new achievements and trends in emerging technologies supporting genomics, transcriptomics and proteomics analyses such as high-throughput sequencing and data managing, text and data-mining, ontologies and Grid technologies. Papers selected for publication, in this supplement to BMC Bioinformatics, cover a broad range of the topics treated, providing also an overview of the main bioinformatics research fields that the EMBnet community is involved in

phiSITE: database of gene regulation in bacteriophages

We have developed phiSITE, database of gene regulation in bacteriophages. To date it contains detailed information about more than 700 experimentally confirmed or predicted regulatory elements (promoters, operators, terminators and attachment sites) from 32 bacteriophages belonging to Siphoviridae, Myoviridae and Podoviridae families. The database is manually curated, the data are collected mainly form scientific papers, cross-referenced with other database resources (EMBL, UniProt, NCBI taxonomy database, NCBI Genome, ICTVdb, PubMed Central) and stored in SQL based database system. The system provides full text search for regulatory elements, graphical visualization of phage genomes and several export options. In addition, visualizations of gene regulatory networks for five phages (Bacillus phage GA-1, Enterobacteria phage lambda, Enterobacteria phage Mu, Enterobacteria phage P2 and Mycoplasma phage P1) have been defined and made available. The phiSITE is accessible at http://www.phisite.org/

Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Gut diversity and the resistome as biomarkers of febrile neutropenia outcome in paediatric oncology patients undergoing hematopoietic stem cell transplantation

Abstract The gut microbiota of paediatric oncology patients undergoing a conditioning regimen before hematopoietic stem cell transplantation is recently considered to play role in febrile neutropenia. Disruption of commensal microbiota and evolution of opportune pathogens community carrying a plethora of antibiotic-resistance genes play crucial role. However, the impact, predictive role and association of patient´s gut resistome in the course of the therapy is still to be elucidated. We analysed gut microbiota composition and resistome of 18 paediatric oncology patients undergoing hematopoietic stem cell transplantation, including 12 patients developing febrile neutropenia, hospitalized at The Bone Marrow Transplantation Unit of the National Institute of Children´s disease in Slovak Republic and healthy individuals (n = 14). Gut microbiome of stool samples obtained in 3 time points, before hematopoietic stem cell transplantation (n = 16), one week after hematopoietic stem cell transplantation (n = 16) and four weeks after hematopoietic stem cell transplantation (n = 14) was investigated using shotgun metagenome sequencing and bioinformatical analysis. We identified significant decrease in alpha-diversity and nine antibiotic-resistance genes msr(C), dfrG, erm(T), VanHAX, erm(B), aac(6)-aph(2), aph(3)-III, ant(6)-Ia and aac(6)-Ii, one week after hematopoietic stem cell transplantation associated with febrile neutropenia. Multidrug-resistant opportune pathogens of ESKAPE, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli found in the gut carried the significant subset of patient’s resistome. Over 50% of patients treated with trimethoprim/sulfamethoxazole, piperacillin/tazobactam and amikacin carried antibiotic-resistance genes to applied treatment. The alpha diversity and the resistome of gut microbiota one week after hematopoietic stem cell transplantation is relevant predictor of febrile neutropenia outcome after hematopoietic stem cell transplantation. Furthermore, the interindividual diversity of multi-drug resistant opportunistic pathogens with variable portfolios of antibiotic-resistance genes indicates necessity of preventive, personalized approach

Uncovering Microbial Composition in Human Breast Cancer Primary Tumour Tissue Using Transcriptomic RNA-seq

Recent research studies are showing breast tissues as a place where various species of microorganisms can thrive and cannot be considered sterile, as previously thought. We analysed the microbial composition of primary tumour tissue and normal breast tissue and found differences between them and between multiple breast cancer phenotypes. We sequenced the transcriptome of breast tumours and normal tissues (from cancer-free women) of 23 individuals from Slovakia and used bioinformatics tools to uncover differences in the microbial composition of tissues. To analyse our RNA-seq data (rRNA depleted), we used and tested Kraken2 and Metaphlan3 tools. Kraken2 has shown higher reliability for our data. Additionally, we analysed 91 samples obtained from SRA database, originated in China and submitted by Sichuan University. In breast tissue, the most enriched group were Proteobacteria, then Firmicutes and Actinobacteria for both datasets, in Slovak samples also Bacteroides, while in Chinese samples Cyanobacteria were more frequent. We have observed changes in the microbiome between cancerous and healthy tissues and also different phenotypes of diseases, based on the presence of circulating tumour cells and few other markers

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Peer reviewed: TrueAcknowledgements: The authors are grateful to all COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies” members for their contribution to the COST Action objectives, and to COST (European Cooperation in Science and Technology) for the economic support, www.cost.eu. WG2 and WG3 thank Emmanuelle Le Chatelier and Pauline Barbet (Université Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France) for preparing the shotgun CRC benchmark dataset.The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices